MRI-guided biopsy of the prostate: correlation between the cancer detection rate and the number of previous negative TRUS biopsies

PURPOSEWe aimed to investigate prostate cancer detection rate of magnetic resonance imaging (MRI)-guided biopsy and to elucidate possible relations to the number of prior negative transrectal ultrasonography (TRUS)-guided biopsies.MATERIALS AND METHODSEighty-seven consecutive patients (mean age, 65.0 years; mean prostate-specific antigen, 13.3 ng/mL) with at least one prior negative TRUS-guided biopsy and persistent suspicion of prostate cancer were included in this study. All patients underwent MRI-guided biopsy after a diagnostic multiparametric MRI examination at 1.5 Tesla. Specimens were immediately fixated and subsequently evaluated by an experienced uropathologist. Prostate cancer detection rates were calculated. Prostate cancer-positive and -negative cores were compared. Correlation between number of prior biopsies and presence of prostate cancer was evaluated.RESULTSCancer detection rates for patients with one (n=24), two (n=25), three (n=18), and four or more (n=20) negative TRUS-guided biopsies were 29.2%, 40.0%, 66.7%, and 35.0%, respectively (P = 0.087). The median number of removed cores per patient was 3 (range, 1–8) without a significant difference between patients with and without cancer (P = 0.48). Thirty of 36 cancer patients were at intermediate or high risk according to the D´Amico clinical risk score. Eleven of 15 high risk cancers were localized in the transition zone (P = 0.002).CONCLUSIONSThis study demonstrates high cancer detection rates of MRI-guided biopsy independent of the number of previous TRUS-guided biopsies and the number of taken prostate cores. MRI-guided biopsy therefore represents a less invasive and effective diagnostic tool for patients with prostate cancer suspicion and previous negative TRUS-guided biopsies

The detrimental role of angiotensin receptor agonistic autoantibodies in intrauterine growth restriction seen in preeclampsia

Growth-restricted fetuses are at risk for a variety of lifelong medical conditions. Preeclampsia, a life-threatening hypertensive disorder of pregnancy, is associated with fetuses who suffer from intrauterine growth restriction (IUGR). Recently, emerging evidence indicates that preeclamptic women harbor AT1 receptor agonistic autoantibodies (AT1-AAs) that contribute to the disease features. However, the exact role of AT1-AAs in IUGR and the underlying mechanisms have not been identified. We report that these autoantibodies are present in the cord blood of women with preeclampsia and retain the ability to activate AT1 receptors. Using an autoantibody-induced animal model of preeclampsia, we show that AT1-AAs cross the mouse placenta, enter fetal circulation, and lead to small fetuses with organ growth retardation. AT1-AAs also induce apoptosis in the placentas of pregnant mice, human villous explants, and human trophoblast cells. Finally, autoantibody-induced IUGR and placental apoptosis are diminished by either losartan or an autoantibody-neutralizing peptide. Thus, these studies identify AT1-AA as a novel causative factor of preeclampsia-associated IUGR and offer two possible underlying mechanisms: a direct detrimental effect on fetal development by crossing the placenta and entering fetal circulation, and indirectly through AT1-AA–induced placental damage. Our findings highlight AT1-AAs as important therapeutic targets

Artificial Intelligence for the Detection of Focal Cortical Dysplasia: Challenges in Translating Algorithms into Clinical Practice

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment

Production of milk foams by steam injection: the effects of steam pressure and nozzle design

Foam properties depend on the physico-chemical characteristics of the continuous phase, the method of production and process conditions employed; however the preparation of barista-style milk foams in coffee shops by injection of steam uses milk as its main ingredient which limits the control of foam properties by changing the biochemical characteristics of the continuous phase. Therefore, the control of process conditions and nozzle design are the only ways available to produce foams with diverse properties. Milk foams were produced employing different steam pressures (100-280 kPa gauge) and nozzle designs (ejector, plunging-jet and confined-jet nozzles). The foamability of milk, and the stability, bubble size and texture of the foams were investigated. Variations in steam pressure and nozzle design changed the hydrodynamic conditions during foam production, resulting in foams having a range of properties. Steam pressure influenced foam characteristics, although the net effect depended on the nozzle design used. These results suggest that, in addition to the physicochemical determinants of milk, the foam properties can also be controlled by changing the steam pressure and nozzle design

Automatic covariance pattern analysis outperforms visual reading of 18 F‐fluorodeoxyglucose‐positron emission tomography (FDG‐PET) in variant progressive supranuclear palsy

Background: To date, studies on positron emission tomography (PET) with F-18-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). Objectives: To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. Methods: This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. Results: Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. Conclusions: Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. (C) 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Neurofilament as a blood marker for diagnosis and monitoring of primary progressive aphasias

Objective: To assess the utility of serum neurofilament for diagnosis and monitoring of primary progressive aphasia (PPA) variants. Methods: We investigated neurofilament light chain (NF-L) levels in blood of 99 patients with PPA (40 with nonfluent variant PPA [nfvPPA], 38 with semantic variant PPA [svPPA], 21 with logopenic variant PPA [lvPPA]) and compared diagnostic performance with that reached by CSF NF-L, phosphorylated neurofilament heavy chain (pNF-H), b-amyloid (Ab(1)-42), tau, and phosphorylated tau. The longitudinal change of blood NF-L levels was measured and analyzed for correlation with functional decline and brain atrophy. Results: Serum NF-L is increased in PPA compared to controls and discriminates between nfvPPA/svPPA and lvPPA with 81% sensitivity and 67% specificity (cutoff 31 pg/mL). CSF NF-L, pNF-H, tau, phosphorylated tau, and Ab1-42 achieved similar performance, and pNF-H was the only marker for discrimination of nfvPPA from svPPA/lvPPA. In most patients with nfvPPA and svPPA, but not lvPPA, serum NF-L increased within follow-up. The increase correlated with functional decline and progression of atrophy of the left frontal lobe of all patients with PPAs and the right middle frontal gyrus of patients with nfvPPA and svPPA. Conclusions: Blood level of NF-L can aid the differential diagnosis of PPA variants, especially in combination with CSF pNF-H. Because serum NF-L correlates with functional decline and atrophy in the disease course, it qualifies as an objective disease status marker. Extended follow-up studies with cases of known neuropathology are imperative

Serum neurofilament light chain in behavioral variant frontotemporal dementia

Objective To determine the association of serum neurofilament light chain (NfL) with functional deterioration and brain atrophy during follow-up of patients with behavioral variant frontotemporal dementia (bvFTD). Methods Blood NfL levels from 74 patients with bvFTD, 26 with Alzheimer disease (AD), 17 with mild cognitive impairment (MCI), and 15 healthy controls (Con) at baseline and follow-up were determined and analyzed for the diagnostic potential in relation to functional assessment (Clinical Dementia Rating Scale Sum of Boxes [CDR-SOB], frontotemporal lobar degeneration-related CDR-SOB, Mini-Mental State Examination [MMSE]) and brain volumetry. Results At baseline, serum NfL level correlated with CSFNfL (bvFTD r = 0.706, p < 0.0001;AD/MCI r = 0.666, p = 0.0003). Highest serum levels were observed in bvFTD (p < 0 0.0001 vs Con and MCI, p = 0.0078 vs AD, respectively). Discrimination of bvFTD from Con/MCI/AD was possible with 91%/74%/74% sensitivity and 79%/74%/58% specificity. At follow-up, serum NfL increased in bvFTD and AD (p = 0.0039 and p = 0.0006, respectively). At baseline and follow-up, NfL correlated with functional scores of patients with bvFTD (e.g., CDR-SOB [baseline] r = 0.4157, p = 0.0006;[follow-up] r = 0.5629, p < 0.0001) and with atrophy in the gray and white matter of many brain regions including frontal and subcortical areas (e.g., frontal lobe: r = -0.5857, p < 0.0001;95% confidence interval -0.7415 to -0.3701). For patients with AD/MCI, NfL correlated with the functional performance as well (e.g., CDR-SOB [baseline] r = 0.6624, p < 0.0001;[follow-up] r = 0.5659, p = 0.0003) but not with regional brain volumes. Conclusions As serum NfL correlates with functional impairment and brain atrophy in bvFTD at different disease stages, we propose it as marker of disease severity, paving the way for its future use as outcome measure for clinical trials. Classification of evidence This study provides Class III evidence that for patients with cognitive problems, serum NfL concentration discriminates bvFTD from other forms of dementia

A large margin algorithm for automated segmentation of white matter hyperintensity

Precise detection and quantification of white matter hyperintensity (WMH) is of great interest in studies of neurological and vascular disorders. In this work, we propose a novel method for automatic WMH segmentation with both supervised and semi-supervised large margin algorithms provided by the framework. The proposed algorithms optimize a kernel based max-margin objective function which aims to maximize the margin between inliers and outliers. We show that the semi-supervised learning problem can be formulated to learn a classifier and label assignment simultaneously, which can be solved efficiently by an iterative algorithm. The model is learned first via the supervised approach and then fine-tuned on a target image by using the semi-supervised algorithm. We evaluate our method on 88 brain fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images from subjects with vascular disease. Quantitative evaluation of the proposed approach shows that it outperforms other well known methods for WMH segmentation